Enhancing Clinical Data Management Through Barcode Integration and Research Electronic Data Capture: Scalable and Adaptable Implementation Study.
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| Názov: | Enhancing Clinical Data Management Through Barcode Integration and Research Electronic Data Capture: Scalable and Adaptable Implementation Study. |
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| Autori: | Zhang R; Department of Electrical and Computer Engineering, School of Engineering, Vanderbilt University, PMB 351824, 2301 Vanderbilt Place, Nashville, TN, 37235-1824, United States, 1 6153222338., Chiron S; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, United States., Tyree R; Department of Medicine, Division of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University Medical Center, Nashville, TN, United States., Carson K; Department of Medicine, Division of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University Medical Center, Nashville, TN, United States., Raber L; Department of Medicine, Division of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University Medical Center, Nashville, TN, United States., Ramadass K; Department of Electrical and Computer Engineering, School of Engineering, Vanderbilt University, PMB 351824, 2301 Vanderbilt Place, Nashville, TN, 37235-1824, United States, 1 6153222338., Gao C; Department of Electrical and Computer Engineering, School of Engineering, Vanderbilt University, PMB 351824, 2301 Vanderbilt Place, Nashville, TN, 37235-1824, United States, 1 6153222338., Kim ME; Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States., Zuo L; Department of Electrical and Computer Engineering, School of Engineering, Vanderbilt University, PMB 351824, 2301 Vanderbilt Place, Nashville, TN, 37235-1824, United States, 1 6153222338., Li Y; Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, United States., Wan Z; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States., Harris PA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States.; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States., Liu Q; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States.; Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, United States., Lau KS; Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.; Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, United States.; Department of Cell and Developmental Biology, School of Medicine, Vanderbilt University, Nashville, TN, United States., Coburn LA; Department of Medicine, Division of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University Medical Center, Nashville, TN, United States.; Vanderbilt Center for Mucosal Inflammation and Cancer, Nashville, TN, United States.; Program of Cancer Biology, School of Medicine, Vanderbilt University, Nashville, TN, United States.; VA Tennessee Valley Healthcare System, Nashville, TN, United States., Wilson KT; Department of Medicine, Division of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University Medical Center, Nashville, TN, United States.; Vanderbilt Center for Mucosal Inflammation and Cancer, Nashville, TN, United States.; Program of Cancer Biology, School of Medicine, Vanderbilt University, Nashville, TN, United States.; VA Tennessee Valley Healthcare System, Nashville, TN, United States.; Department of Pathology, Microbiology, and Immunology,, Vanderbilt University Medical Center, Nashville, TN, United States., Huo Y; Department of Electrical and Computer Engineering, School of Engineering, Vanderbilt University, PMB 351824, 2301 Vanderbilt Place, Nashville, TN, 37235-1824, United States, 1 6153222338.; Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States., Landman BA; Department of Electrical and Computer Engineering, School of Engineering, Vanderbilt University, PMB 351824, 2301 Vanderbilt Place, Nashville, TN, 37235-1824, United States, 1 6153222338.; Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States., Bao S; Department of Electrical and Computer Engineering, School of Engineering, Vanderbilt University, PMB 351824, 2301 Vanderbilt Place, Nashville, TN, 37235-1824, United States, 1 6153222338. |
| Zdroj: | JMIR formative research [JMIR Form Res] 2025 Sep 12; Vol. 9, pp. e70016. Date of Electronic Publication: 2025 Sep 12. |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101726394 Publication Model: Electronic Cited Medium: Internet ISSN: 2561-326X (Electronic) Linking ISSN: 2561326X NLM ISO Abbreviation: JMIR Form Res Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Toronto, ON, Canada : JMIR Publications, [2017]- |
| Výrazy zo slovníka MeSH: | Data Management*/methods , Data Management*/standards , Electronic Data Processing*/methods, Humans ; Electronic Health Records ; Software |
| Abstrakt: | Background: Effective data management is crucial in clinical studies for precise tracking, secure storage, and reliable analysis of samples. Traditional systems often encounter challenges like barcode recognition errors, inadequate data details, and diminished performance under heavy workloads. Objective: This paper aims to enhance clinical data management by improving barcode robustness, increasing data granularity, and boosting system throughput. These improvements address key challenges in barcode informatics systems, as highlighted in previous studies, to better support real clinical applications. In addition, we aim to validate the design criteria on various gastrointestinal-related studies, ensuring it can be easily integrated into other clinical data management workflows. Methods: We evaluated the robustness of various barcode technologies under significant blurring conditions, implemented a dynamic organ-specific archive in the REDCap (Research Electronic Data Capture) database for various clinical study data collection criteria, and used Docker to containerize the informatics software for different studies. In addition, we proposed a local cache system to reduce interaction times with REDCap for large-scale data records. Experimental setups include assessing barcode recognition accuracy under various levels of image blurring, showcasing different study types managed with the organ-specific archive, and measuring system throughput and response times with and without the proposed local cache system. Results: Our findings demonstrate that the DataMatrix barcode exhibits superior resilience, maintaining high recognition accuracy under blurred conditions. The dynamic organ-specific archive in REDCap enabled precise tracking of sample origins, improving data granularity. Docker containerization streamlines software deployment and ensures consistency across studies. The local cache system significantly reduces interaction times with REDCap, decreasing operating time by nearly eightfold compared to the naïve strategy when handling large patient datasets. Conclusions: The proposed enhancements significantly improve barcode robustness, data granularity, and system throughput in the informatics system, addressing key limitations identified in previous studies. These optimizations ensure efficient data management and robust support for diverse clinical research needs. (© Rendong Zhang, Sophie Chiron, Regina Tyree, Kate Carson, Larry Raber, Karthik Ramadass, Chenyu Gao, Michael E Kim, Lianrui Zuo, Yike Li, Zhiyu Wan, Paul A Harris, Qi Liu, Ken S Lau, Lori A Coburn, Keith T Wilson, Yuankai Huo, Bennett A Landman, Shunxing Bao. Originally published in JMIR Formative Research (https://formative.jmir.org).) |
| References: | J Biomed Inform. 2009 Apr;42(2):377-81. (PMID: 18929686) Diagnostics (Basel). 2022 Feb 18;12(2):. (PMID: 35204617) J Clin Pathol. 2023 Oct;76(10):659-663. (PMID: 37532289) J Clin Med. 2020 Nov 18;9(11):. (PMID: 33217963) Proc SPIE Int Soc Opt Eng. 2021;11601:. (PMID: 34539029) Pathol Res Pract. 2020 Sep;216(9):153040. (PMID: 32825928) J Clin Transl Sci. 2020 Feb 06;4(2):108-114. (PMID: 32313700) J Biomed Inform. 2019 Jul;95:103208. (PMID: 31078660) J Digit Imaging. 2017 Oct;30(5):555-560. (PMID: 28116576) JAMIA Open. 2021 May 20;4(2):ooab027. (PMID: 34549169) Int J Med Inform. 2018 Nov;119:54-60. (PMID: 30342686) Nat Rev Clin Oncol. 2019 Nov;16(11):703-715. (PMID: 31399699) Implement Sci Commun. 2022 Jan 28;3(1):6. (PMID: 35090575) Arch Pathol Lab Med. 2020 Feb;144(2):221-228. (PMID: 31295015) Pathology. 2019 Jan;51(1):1-10. (PMID: 30522785) Histopathology. 2017 Jan;70(1):134-145. (PMID: 27960232) Diagnostics (Basel). 2021 Nov 22;11(11):. (PMID: 34829514) Int J Environ Res Public Health. 2022 Dec 23;20(1):. (PMID: 36612513) Rev Recent Clin Trials. 2019;14(1):10-23. (PMID: 30251611) |
| Grant Information: | UL1 TR000445 United States TR NCATS NIH HHS; R01 DK128200 United States DK NIDDK NIH HHS; P30 DK058404 United States DK NIDDK NIH HHS; I01 CX002171 United States CX CSRD VA; UL1 RR024975 United States RR NCRR NIH HHS; I01 BX004366 United States BX BLRD VA; R01 DK103831 United States DK NIDDK NIH HHS; I01 CX002473 United States CX CSRD VA; I01 CX002662 United States CX CSRD VA |
| Contributed Indexing: | Keywords: Docker containerization; REDCap; barcode technology; clinical data management; data granularity; system throughput optimization |
| Entry Date(s): | Date Created: 20250915 Date Completed: 20250916 Latest Revision: 20250923 |
| Update Code: | 20250923 |
| PubMed Central ID: | PMC12434633 |
| DOI: | 10.2196/70016 |
| PMID: | 40951995 |
| Databáza: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Background: Effective data management is crucial in clinical studies for precise tracking, secure storage, and reliable analysis of samples. Traditional systems often encounter challenges like barcode recognition errors, inadequate data details, and diminished performance under heavy workloads.<br />Objective: This paper aims to enhance clinical data management by improving barcode robustness, increasing data granularity, and boosting system throughput. These improvements address key challenges in barcode informatics systems, as highlighted in previous studies, to better support real clinical applications. In addition, we aim to validate the design criteria on various gastrointestinal-related studies, ensuring it can be easily integrated into other clinical data management workflows.<br />Methods: We evaluated the robustness of various barcode technologies under significant blurring conditions, implemented a dynamic organ-specific archive in the REDCap (Research Electronic Data Capture) database for various clinical study data collection criteria, and used Docker to containerize the informatics software for different studies. In addition, we proposed a local cache system to reduce interaction times with REDCap for large-scale data records. Experimental setups include assessing barcode recognition accuracy under various levels of image blurring, showcasing different study types managed with the organ-specific archive, and measuring system throughput and response times with and without the proposed local cache system.<br />Results: Our findings demonstrate that the DataMatrix barcode exhibits superior resilience, maintaining high recognition accuracy under blurred conditions. The dynamic organ-specific archive in REDCap enabled precise tracking of sample origins, improving data granularity. Docker containerization streamlines software deployment and ensures consistency across studies. The local cache system significantly reduces interaction times with REDCap, decreasing operating time by nearly eightfold compared to the naïve strategy when handling large patient datasets.<br />Conclusions: The proposed enhancements significantly improve barcode robustness, data granularity, and system throughput in the informatics system, addressing key limitations identified in previous studies. These optimizations ensure efficient data management and robust support for diverse clinical research needs.<br /> (© Rendong Zhang, Sophie Chiron, Regina Tyree, Kate Carson, Larry Raber, Karthik Ramadass, Chenyu Gao, Michael E Kim, Lianrui Zuo, Yike Li, Zhiyu Wan, Paul A Harris, Qi Liu, Ken S Lau, Lori A Coburn, Keith T Wilson, Yuankai Huo, Bennett A Landman, Shunxing Bao. Originally published in JMIR Formative Research (https://formative.jmir.org).) |
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| ISSN: | 2561-326X |
| DOI: | 10.2196/70016 |